用于二进制分类的 R 中的 Xgboost 超参数调整

Question

我是 R 新手，正在尝试为 xgboost-二进制分类进行超参数调整，但是我遇到了错误，如果有人可以帮助我，我将不胜感激

as.matrix(cv.res)[, 3] 中的错误：下标超出范围此外：警告消息：不推荐使用“early.stop.round”。请改用“early_stopping_rounds”。请参阅 help("Deprecated") 和 help("xgboost-deprecated")。

请在下面找到代码sn-p`

 I would appreciate if some one could provide another alternative too apart from this approach in R

X_Train <- as(X_train, "dgCMatrix")


GS_LogLoss = data.frame("Rounds" = numeric(), 
                        "Depth" = numeric(),
                        "r_sample" = numeric(),
                        "c_sample" = numeric(), 
                        "minLogLoss" = numeric(),
                        "best_round" = numeric())

for (rounds in seq(50,100, 25)) {
  
  for (depth in c(4, 6, 8, 10)) {
    
    for (r_sample in c(0.5, 0.75, 1)) {
      
      for (c_sample in c(0.4, 0.6, 0.8, 1)) {
        
        for (imb_scale_pos_weight in c(5, 10, 15, 20, 25))	{
          
          for (wt_gamma in c(5, 7, 10)) {
            
            for (wt_max_delta_step in c(5,7,10)) {
              
              for (wt_min_child_weight in c(5,7,10,15))	{
                
                
                set.seed(1024)
                eta_val = 2 / rounds
                cv.res = xgb.cv(data = X_Train, nfold = 2, label = y_train, 
                                nrounds = rounds, 
                                eta = eta_val, 
                                max_depth = depth,
                                subsample = r_sample, 
                                colsample_bytree = c_sample,
                                early.stop.round = 0.5*rounds,
                                scale_pos_weight= imb_scale_pos_weight,
                                max_delta_step = wt_max_delta_step,
                                gamma = wt_gamma,
                                objective='binary:logistic', 
                                eval_metric = 'auc',
                                verbose = FALSE)
                
                print(paste(rounds, depth, r_sample, c_sample, min(as.matrix(cv.res)[,3]) ))
                GS_LogLoss[nrow(GS_LogLoss)+1, ] = c(rounds, 
                                                     depth, 
                                                     r_sample, 
                                                     c_sample, 
                                                     min(as.matrix(cv.res)[,3]), 
                                                     which.min(as.matrix(cv.res)[,3]))
                
              }
            }
          }
        }	
      }
    }
  }	
}

`

Answer 1

要选择超参数，您可以使用元包tidymodels，尤其是包parsnip、rsample、yardstick 和tune。

这样的工作流程可以工作：

library(tidyverse)
library(tidymodels)

# Specify the model and the parameters to tune (parnsip)
model <-
  boost_tree(tree_depth = tune(), mtry = tune()) %>% 
  set_mode("classification") %>% 
  set_engine("xgboost")

# Specify the resampling method (rsample)
splits <- vfold_cv(X_train, v = 2)

# Specify the metrics to optimize (yardstick)
metrics <- metric_set(roc_auc)

# Specify the parameters grid (or you can use dials to automate your grid search)
grid <- expand_grid(tree_depth = c(4, 6, 8, 10),
                    mtry = c(2, 10, 50)) # You can add others

# Run each model (tune)
tuned <- tune_grid(formula = Y ~ .,
                   model = model,
                   resamples = splits,
                   grid = grid,
                   metrics = metrics,
                   control = control_grid(verbose = TRUE))

# Check results
show_best(tuned)
autoplot(tuned)
select_best(tuned)

# Update model
tuned_model <- 
  model %>% 
  finalize_model(select_best(tuned)) %>% 
  fit(Y ~ ., data = X_train)

# Make prediction 
predict(tuned_model, X_train)
predict(tuned_model, X_test)

请注意，模型规范中的名称与xgboost 中的原始名称相比可能会发生变化，因为parsnip 是一个统一的接口，在多个模型中具有一致的名称。见here。